Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents' expectations. The research focuses on experts' expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents' expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents' judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance. We obtain the best results for Belgium, Norway, Austria, Lithuania, Japan and the United Kingdom.

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“…We use the Distributed Evolutionary Algorithms Package (DEAP) framework implemented in Python (Fortin et al, 2012;Gong et al, 2015).…”

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confidence: 99%

A new approach for the quantification of qualitative measures of economic expectations

2016

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“…It is worth mentioning that parallel evolutionary algorithms are suitable for the problems of a high dimensionality or of complex and time-consuming computation features [51], such as large-scale air traffic flow optimization [52], discrete resource allocation in classic economic field [53], and large-scale function optimization [54] [55]. They are adopted to either speed up the optimization or enhance the solution quality through a dimensionality reduction strategy.…”

Section: Comparison With Parallel Algorithmsmentioning

confidence: 99%

Kuhn–Munkres Parallel Genetic Algorithm for the Set Cover Problem and Its Application to Large-Scale Wireless Sensor Networks

Zhang¹,

Zhang

Gong³

et al. 2016

IEEE Trans. Evol. Computat.

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Kuhn-Munkres parallel genetic algorithm for the set cover problem and its application to large-scale wireless sensor networks. IEEE Transactions on Evolutionary Computation.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/118977/ Abstract-Operating mode scheduling is crucial for the lifetime of wireless sensor networks. However, the growing scale of networks has made such a scheduling problem more and more challenging, as existing set cover and evolutionary algorithms become unable to provide satisfactory efficiency due to the curse of dimensionality. In this paper, a Kuhn-Munkres parallel genetic algorithm is developed to solve the set cover problem and is applied to lifetime maximization of large-scale wireless sensor networks. The proposed algorithm schedules the sensors into a number of disjoint complete cover sets and activates them in batch for energy conservation. It uses a divide-and-conquer strategy of dimensionality reduction, and the polynomial Kuhn-Munkres algorithm are hence adopted to splice the feasible solutions obtained in each subarea to enhance the search efficiency substantially. To further improve global efficiency, a redundant-trend sensor schedule strategy is developed. Additionally, we meliorate the evaluation function through penalizing incomplete cover sets, which speeds up convergence. Eight types of experiments are conducted on a distributed platform to test and inform the effectiveness of the proposed algorithm. The results show that it offers promising performance in terms of the convergence rate, solution quality, and success rate.Index Terms-parallel genetic algorithm, set cover problem, large-scale wireless sensor networks, Kuhn-Munkres algorithm.

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“…Consistency Based Feature Selection (CBFS). Gene Expressions based validations have done in this Scheme, which shown in the detailed procedure [5], [25]. The Forward Greedy Search Algorithm based Gaussian Kernel Approximation [4], [18][19][20] was designed as follows.…”

Section: Kernelized Fuzzy Rough Set Based Semi Supervised Support Vecmentioning

confidence: 99%

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

Subasree

Gopalan²,

Sakthivel

2018

IJET

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Microarray based Cancer Pattern Classification is one of the popular techniques in Bioinformatics Research. This Research Work is noticed that for studying the expression levels through the Gene Expression profiling experiments, thousands of Genes have to be simultaneously studied to understand the patterns of the Gene Expression or Cancer Pattern. This research work proposed an efficient Cancer Pattern Clas-sifier called An Enhanced Multi-Objective Pswarm (EMOPS) and it is studied thoroughly in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. The results were compared with the recently proposed classifiers namely Hybrid Ant Bee Algorithm (HABA), Kernelized Fuzzy Rough Set Based Semi Supervised Support Vector Machine (KFRS-S3VM) and Multi-objective Particle Swarm Optimization (MPSO). For analyzing the performances of the proposed model, this work considered a few cancer patterns namely Bladder, Breast, Colon, Endometrial, Kidney, Leukemia, Lung, Melanoma, Mom-Hodgkin, Pancreatic, Prostate and Thyroid. From our experimental results, it was noticed that the proposed model outperforms the identified three classifiers in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. To improve the performance of the system further in term of Processing Time, the proposed model Enhanced Multi-Objective Pswarm (EMOPS) is implemented under Parallel Framework and evaluated. That is the model is tested with Two, Four, Eight and Sixteen Parallel Processors and from the results, it is established that the Processing Time decreases considerably which will improve the performance of the Proposed Model.

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Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

Cited by 332 publications

References 146 publications

A new approach for the quantification of qualitative measures of economic expectations

A new approach for the quantification of qualitative measures of economic expectations

Kuhn–Munkres Parallel Genetic Algorithm for the Set Cover Problem and Its Application to Large-Scale Wireless Sensor Networks

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

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